The space-domain news article corpus contains 2.8M sentences across 48.5k documents. To identify a shortlist of candidate sentences for human labeling that describe space events of interest (launches, failures, and decommissionings), we constructed a set of high-recall dependency-based extraction rules using the Odinson Information Extraction language [1]}. Odinson allows expressing extraction rules as a combination of syntactic dependencies, named entities, part-of-speech tags, and surface forms, and processes documents approximately 5 orders of magnitude faster than comparable approaches [2]}, [3]}, scanning and extracting sentences in our nearly 50k document corpus in approximately 4 minutes on inexpensive desktop hardware. Articles were indexed using the Odinson indexer, where Stanford CoreNLP [4]} supplied tagging, lemmatization, and dependency parsing.
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